OpenRaiser/Intern-Atlas huggingface dataset:
Towards Tracking Data Flows in Cloud Architectures
Venue: arXiv cs.CR 2020
Year: 2020

As cloud services become central in an increasing number of applications, they process and store more personal and business-critical data. At the same time, privacy and compliance regulations such as GDPR, the EU ePrivacy regulation, PCI, and the upcoming EU Cybersecurity Act raise the bar for secure processing and traceability of critical data. Especially the demand to provide information about existing data records of an individual and the ability to delete them on demand is central in privacy regulations. Common to these requirements is that cloud providers must be able to track data as it flows across the different services to ensure that it never moves outside of the legitimate realm, and it is known at all times where a specific copy of a record that belongs to a specific individual or business process is located. However, current cloud architectures do neither provide the means to holistically track data flows across different services nor to enforce policies on data flows. In this paper, we point out the deficits in the data flow tracking functionalities of major cloud providers by means of a set of practical experiments. We then generalize from these experiments introducing a generic architecture that aims at solving the problem of cloud-wide data flow tracking and show how it can be built in a Kubernetes-based prototype implementation.
Distillation Guided Residual Learning for Binary Convolutional Neural Networks
Venue: arXiv cs.CV 2020
Year: 2020

It is challenging to bridge the performance gap between Binary CNN (BCNN) and Floating point CNN (FCNN). We observe that, this performance gap leads to substantial residuals between intermediate feature maps of BCNN and FCNN. To minimize the performance gap, we enforce BCNN to produce similar intermediate feature maps with the ones of FCNN. This training strategy, i.e., optimizing each binary convolutional block with block-wise distillation loss derived from FCNN, leads to a more effective optimization to BCNN. It also motivates us to update the binary convolutional block architecture to facilitate the optimization of block-wise distillation loss. Specifically, a lightweight shortcut branch is inserted into each binary convolutional block to complement residuals at each block. Benefited from its Squeeze-and-Interaction (SI) structure, this shortcut branch introduces a fraction of parameters, e.g., 10\% overheads, but effectively complements the residuals. Extensive experiments on ImageNet demonstrate the superior performance of our method in both classification efficiency and accuracy, e.g., BCNN trained with our methods achieves the accuracy of 60.45\% on ImageNet.
Battery Recharging Time Models for Reconfigurable Intelligent Surface-Assisted Wireless Power Transfer Systems
Venue: arXiv cs.IT 2020
Year: 2020

In this paper, we develop an analytical framework for the statistical analysis of the battery recharging time (BRT) in reconfigurable intelligent surfaces (RISs) aided wireless power transfer (WPT) systems. Specifically, we derive novel closed-form expressions for the probability density function (PDF), cumulative distribution function, and moments of the BRT of the radio frequency energy harvesting wireless nodes. Moreover, closed-form expressions of the the PDF of the BRT is obtained for two special cases: i) when the RIS is equipped with one reflecting element (RE), ii) when the RIS consists of a large number of REs. Capitalizing on the derived expressions, we offer a comprehensive treatment for the statistical characterization of the BRT and study the impact of the system and battery parameters on its performance. Our results reveal that the proposed statistical models are analytically tractable, accurate, and efficient in assessing the sustainability of RIS-assisted WPT networks and in providing key design insights for large-scale future wireless applications. For example, we demonstrate that a 4-fold reduction in the mean time of the BRT can be achieved by doubling the number of RIS elements. Monte Carlo simulation results corroborate the accuracy of the proposed theoretical framework.
On Distributed Listing of Cliques
Venue: arXiv cs.DC 2020
Year: 2020

We show an $\tilde{O}(n^{p/(p+2)})$-round algorithm in the \congest model for \emph{listing} of $K_p$ (a clique with $p$ nodes), for all $p =4, p\geq 6$. For $p = 5$, we show an $\tilde{O}(n^{3/4})$-round algorithm. For $p=4$ and $p=5$, our results improve upon the previous state-of-the-art of $O(n^{5/6+o(1)})$ and $O(n^{21/22+o(1)})$, respectively, by Eden et al. [DISC 2019]. For all $p\geq 6$, ours is the first sub-linear round algorithm for $K_p$ listing. We leverage the recent expander decomposition algorithm of Chang et al. [SODA 2019] to create clusters with a good mixing time. Three key novelties in our algorithm are: (1) we carefully iterate our listing process with coupled values of min-degree within the clusters and arboricity outside the clusters, (2) all the listing is done within the cluster, which necessitates new techniques for bringing into the cluster the information about \emph{all} edges that can potentially form $K_p$ instances with the cluster edges, and (3) within each cluster we use a sparsity-aware listing algorithm, which is faster than a general listing algorithm and which we can allow the cluster to use since we make sure to sparsify the graph as the iterations proceed. As a byproduct of our algorithm, we show an \emph{optimal} sparsity-aware algorithm for $K_p$ listing, which runs in $\tildeΘ(1 + m/n^{1 + 2/p})$ rounds in the \clique model. Previously, Pandurangan et al. [SPAA 2018], Chang et al. [SODA 2019], and Censor-Hillel et al. [TCS 2020] showed sparsity-aware algorithms for the case of $p = 3$, yet ours is the first such sparsity aware algorithm for $p \geq 4$.
An Upper Bound on the Error Induced by Saddlepoint Approximations -- Applications to Information Theory
Venue: arXiv cs.IT 2020
Year: 2020

This paper introduces an upper bound on the absolute difference between: (a) the cumulative distribution function (CDF) of the sum of a finite number of independent and identically distributed random variables with finite absolute third moment; and (b) a saddlepoint approximation of such CDF. This upper bound, which is particularly precise in the regime of large deviations, is used to study the dependence testing (DT) bound and the meta converse (MC) bound on the decoding error probability (DEP) in point-to-point memoryless channels. Often, these bounds cannot be analytically calculated and thus lower and upper bounds become particularly useful. Within this context, the main results include, respectively, new upper and lower bounds on the DT and MC bounds. A numerical experimentation of these bounds is presented in the case of the binary symmetric channel, the additive white Gaussian noise channel, and the additive symmetric $α$-stable noise channel.
Evaluating the Apperception Engine
Venue: arXiv cs.AI 2020
Year: 2020

The Apperception Engine is an unsupervised learning system. Given a sequence of sensory inputs, it constructs a symbolic causal theory that both explains the sensory sequence and also satisfies a set of unity conditions. The unity conditions insist that the constituents of the theory - objects, properties, and laws - must be integrated into a coherent whole. Once a theory has been constructed, it can be applied to predict future sensor readings, retrodict earlier readings, or impute missing readings. In this paper, we evaluate the Apperception Engine in a diverse variety of domains, including cellular automata, rhythms and simple nursery tunes, multi-modal binding problems, occlusion tasks, and sequence induction intelligence tests. In each domain, we test our engine's ability to predict future sensor values, retrodict earlier sensor values, and impute missing sensory data. The engine performs well in all these domains, significantly outperforming neural net baselines and state of the art inductive logic programming systems. These results are significant because neural nets typically struggle to solve the binding problem (where information from different modalities must somehow be combined together into different aspects of one unified object) and fail to solve occlusion tasks (in which objects are sometimes visible and sometimes obscured from view). We note in particular that in the sequence induction intelligence tests, our system achieved human-level performance. This is notable because our system is not a bespoke system designed specifically to solve intelligence tests, but a general-purpose system that was designed to make sense of any sensory sequence.
Towards Large Intelligent Surface (LIS)-based Communications
Venue: arXiv cs.IT 2020
Year: 2020

The concept of large intelligent surface (LIS)-based communication has recently raised research attention, in which a LIS is regarded as an antenna array whose entire surface area can be used for radio signal transmission and reception. To provide a fundamental understanding of LIS-based communication, this paper studies the uplink (UL) performance of LIS-based communication with matched filtering. We first investigate the new properties introduced by LIS. In particular, the array gain, spatial resolution, and the capability of interference suppression are theoretically presented and characterized. Then, we study two possible LIS system layouts in terms of UL, i.e., centralized LIS (C-LIS) and distributed LIS (D-LIS). Our analysis showcases that a centralized system has strong capability of interference suppression; in fact, interference can nearly be eliminated if the surface area is sufficient large or the frequency band is sufficient high. For D-LIS, we propose a series of resource allocation algorithms, including user association scheme, orientation control, and power control, to extend the coverage area of a distributed system. Simulation results show that the proposed algorithms significantly improve the system performance, and even more importantly, we observe that D-LIS outperforms CLIS in microwave bands, while C-LIS is superior to D-LIS in mmWave bands. These observations serve as useful guidelines for practical LIS deployments.
VRUNet: Multi-Task Learning Model for Intent Prediction of Vulnerable Road Users
Venue: arXiv cs.CV 2020
Year: 2020

Advanced perception and path planning are at the core for any self-driving vehicle. Autonomous vehicles need to understand the scene and intentions of other road users for safe motion planning. For urban use cases it is very important to perceive and predict the intentions of pedestrians, cyclists, scooters, etc., classified as vulnerable road users (VRU). Intent is a combination of pedestrian activities and long term trajectories defining their future motion. In this paper we propose a multi-task learning model to predict pedestrian actions, crossing intent and forecast their future path from video sequences. We have trained the model on naturalistic driving open-source JAAD dataset, which is rich in behavioral annotations and real world scenarios. Experimental results show state-of-the-art performance on JAAD dataset and how we can benefit from jointly learning and predicting actions and trajectories using 2D human pose features and scene context.
Impression Space from Deep Template Network
Venue: arXiv cs.CV 2020
Year: 2020

It is an innate ability for humans to imagine something only according to their impression, without having to memorize all the details of what they have seen. In this work, we would like to demonstrate that a trained convolutional neural network also has the capability to "remember" its input images. To achieve this, we propose a simple but powerful framework to establish an {\emph{Impression Space}} upon an off-the-shelf pretrained network. This network is referred to as the {\emph{Template Network}} because its filters will be used as templates to reconstruct images from the impression. In our framework, the impression space and image space are bridged by a layer-wise encoding and iterative decoding process. It turns out that the impression space indeed captures the salient features from images, and it can be directly applied to tasks such as unpaired image translation and image synthesis through impression matching without further network training. Furthermore, the impression naturally constructs a high-level common space for different data. Based on this, we propose a mechanism to model the data relations inside the impression space, which is able to reveal the feature similarity between images. Our code will be released.
Not Your Grandfathers Test Set: Reducing Labeling Effort for Testing
Venue: arXiv cs.LG 2020
Year: 2020

Building and maintaining high-quality test sets remains a laborious and expensive task. As a result, test sets in the real world are often not properly kept up to date and drift from the production traffic they are supposed to represent. The frequency and severity of this drift raises serious concerns over the value of manually labeled test sets in the QA process. This paper proposes a simple but effective technique that drastically reduces the effort needed to construct and maintain a high-quality test set (reducing labeling effort by 80-100% across a range of practical scenarios). This result encourages a fundamental rethinking of the testing process by both practitioners, who can use these techniques immediately to improve their testing, and researchers who can help address many of the open questions raised by this new approach.
THEMIS: Decentralized and Trustless Ad Platform with Reporting Integrity
Venue: arXiv cs.CR 2020
Year: 2020

Online advertising fuels the (seemingly) free internet. However, although users can access most websites free of charge, they need to pay a heavy cost on their privacy and blindly trust third parties and intermediaries that absorb great amounts of adrevenues and user data. This is one of the reasons users opt out from advertising by resorting ad blockers thatin turn cost publishers millions of dollars in lost adrevenues. Existing privacy-preserving advertising approaches(e.g., Adnostic, Privad, Brave Ads) from both industry and academia cannot guarantee the integrity of the performance analytics they provide to advertisers, while they also rely on centralized management that users have to trust without being able to audit. In this paper, we propose THEMIS, a novel privacy-by-design ad platform that is decentralized and requires zero trust from users. THEMIS (i) provides auditability to all participants, (ii) rewards users for viewing ads, and (iii) allows advertisers to verify the performance and billing reports of their ad campaigns. To demonstrate the feasibility and practicability of our approach, we implemented a prototype of THEMIS using a combination of smart contracts and zero-knowledge schemes. Performance evaluation results show that during adreward payouts, THEMIS can support more than 51M users on a single-sidechain setup or 153M users ona multi-sidechain setup, thus proving that THEMIS scales linearly.
Contrastive Training for Improved Out-of-Distribution Detection
Venue: arXiv cs.LG 2020
Year: 2020

Reliable detection of out-of-distribution (OOD) inputs is increasingly understood to be a precondition for deployment of machine learning systems. This paper proposes and investigates the use of contrastive training to boost OOD detection performance. Unlike leading methods for OOD detection, our approach does not require access to examples labeled explicitly as OOD, which can be difficult to collect in practice. We show in extensive experiments that contrastive training significantly helps OOD detection performance on a number of common benchmarks. By introducing and employing the Confusion Log Probability (CLP) score, which quantifies the difficulty of the OOD detection task by capturing the similarity of inlier and outlier datasets, we show that our method especially improves performance in the `near OOD' classes -- a particularly challenging setting for previous methods.
Partial Altruism is Worse than Complete Selfishness in Nonatomic Congestion Games
Venue: arXiv cs.GT 2020
Year: 2020

We seek to understand the fundamental mathematics governing infrastructure-scale interactions between humans and machines, particularly when the machines' intended purpose is to influence and optimize the behavior of the humans. To that end, this paper investigates the worst-case congestion that can arise in nonatomic network congestion games when a fraction of the traffic is completely altruistic (e.g., benevolent self-driving cars) and the remainder is completely selfish (e.g., human commuters). We study the worst-case harm of altruism in such scenarios in terms of the perversity index, or the worst-case equilibrium congestion cost resulting from the presence of altruistic traffic, relative to the congestion cost which would result if all traffic were selfish. We derive a tight bound on the perversity index for the class of series-parallel network congestion games with convex latency functions, and show three facts: First, the harm of altruism is maximized when exactly half of the traffic is altruistic, but it gracefully vanishes when the fraction of altruistic traffic approaches either 0 or 1. Second, we show that the harm of altruism is linearly increasing in a natural measure of the "steepness" of network latency functions. Finally, we show that for any nontrivial fraction of altruistic traffic, the harm of altruism exceeds the price of anarchy associated with all-selfish traffic: in a sense, partial altruism is worse than complete selfishness.
Attention-guided Quality Assessment for Automated Cryo-EM Grid Screening
Venue: arXiv cs.CV 2020
Year: 2020

Cryogenic electron microscopy (cryo-EM) has become an enabling technology in drug discovery and in understanding molecular bases of disease by producing near-atomic resolution (less than 0.4 nm) 3D reconstructions of biological macromolecules. The imaging process required for 3D reconstructions involves a highly iterative and empirical screening process, starting with the acquisition of low magnification images of the cryo-EM grids. These images are inspected for squares that are likely to contain useful molecular signals. Potentially useful squares within the grid are then imaged at progressively higher magnifications, with the goal of identifying sub-micron areas within circular holes (bounded by the squares) for imaging at high magnification. This arduous, multi-step data acquisition process represents a bottleneck for obtaining a high throughput data collection. Here, we focus on automating the early decision making for the microscope operator, scoring low magnification images of squares, and proposing the first deep learning framework, XCryoNet, for automated cryo-EM grid screening. XCryoNet is a semi-supervised, attention-guided deep learning approach that provides explainable scoring of automatically extracted square images using limited amounts of labeled data. Results show up to 8% and 37% improvements over a fully supervised and a no-attention solution, respectively, when labeled data is scarce.
Learning Local Complex Features using Randomized Neural Networks for Texture Analysis
Venue: arXiv cs.CV 2020
Year: 2020

Texture is a visual attribute largely used in many problems of image analysis. Currently, many methods that use learning techniques have been proposed for texture discrimination, achieving improved performance over previous handcrafted methods. In this paper, we present a new approach that combines a learning technique and the Complex Network (CN) theory for texture analysis. This method takes advantage of the representation capacity of CN to model a texture image as a directed network and uses the topological information of vertices to train a randomized neural network. This neural network has a single hidden layer and uses a fast learning algorithm, which is able to learn local CN patterns for texture characterization. Thus, we use the weighs of the trained neural network to compose a feature vector. These feature vectors are evaluated in a classification experiment in four widely used image databases. Experimental results show a high classification performance of the proposed method when compared to other methods, indicating that our approach can be used in many image analysis problems.
Capacity Improvement in Wideband Reconfigurable Intelligent Surface-Aided Cell-Free Network
Venue: arXiv cs.IT 2020
Year: 2020

Thanks to the strong ability against the inter-cell interference, cell-free network has been considered as a promising technique to improve the network capacity of future wireless systems. However, for further capacity enhancement, it requires to deploy more base stations (BSs) with high cost and power consumption. To address the issue, inspired by the recently proposed technique called reconfigurable intelligent surface (RIS), we propose the concept of RIS-aided cell-free network to improve the network capacity with low cost and power consumption. Then, for the proposed RIS-aided cell-free network in the typical wideband scenario, we formulate the joint precoding design problem at the BSs and RISs to maximize the network capacity. Due to the non-convexity and high complexity of the formulated problem, we develop an alternating optimization algorithm to solve this challenging problem. Note that most of the considered scenarios in existing works are special cases of the general scenario in this paper, and the proposed joint precoding framework can also serve as a general solution to maximize the capacity in most of existing RIS-aided scenarios. Finally, simulation results verify that, compared with the conventional cell-free network, the network capacity of the proposed scheme can be improved significantly.
Batch-level Experience Replay with Review for Continual Learning
Venue: arXiv cs.LG 2020
Year: 2020

Continual learning is a branch of deep learning that seeks to strike a balance between learning stability and plasticity. The CVPR 2020 CLVision Continual Learning for Computer Vision challenge is dedicated to evaluating and advancing the current state-of-the-art continual learning methods using the CORe50 dataset with three different continual learning scenarios. This paper presents our approach, called Batch-level Experience Replay with Review, to this challenge. Our team achieved the 1'st place in all three scenarios out of 79 participated teams. The codebase of our implementation is publicly available at https://github.com/RaptorMai/CVPR20_CLVision_challenge
Do We Need Sound for Sound Source Localization?
Venue: arXiv cs.CV 2020
Year: 2020

During the performance of sound source localization which uses both visual and aural information, it presently remains unclear how much either image or sound modalities contribute to the result, i.e. do we need both image and sound for sound source localization? To address this question, we develop an unsupervised learning system that solves sound source localization by decomposing this task into two steps: (i) "potential sound source localization", a step that localizes possible sound sources using only visual information (ii) "object selection", a step that identifies which objects are actually sounding using aural information. Our overall system achieves state-of-the-art performance in sound source localization, and more importantly, we find that despite the constraint on available information, the results of (i) achieve similar performance. From this observation and further experiments, we show that visual information is dominant in "sound" source localization when evaluated with the currently adopted benchmark dataset. Moreover, we show that the majority of sound-producing objects within the samples in this dataset can be inherently identified using only visual information, and thus that the dataset is inadequate to evaluate a system's capability to leverage aural information. As an alternative, we present an evaluation protocol that enforces both visual and aural information to be leveraged, and verify this property through several experiments.